Field of the Invention
The present disclosure relates generally to methods for detecting tampering in images. More particularly, aspects of the present disclosure relate to systems and methods for detecting inpainting forgery in digital images, as well as combating anti-forensics.
Description of the Related Art
With the rapid development of multimedia and network, enormous digital multimedia data are daily created and widely spread in the world wide. While the standards of our life and education are greatly improved, as well as many other things including our needs and wants, these data are easily manipulated for malicious or criminal intent, raising serious concern and realistic threats in our society and posing many challenges in digital forensics and information security.
In multimedia forensics, the detection of forgery on joint photographic experts group (JPEG) images is meaningful and challenging work. While being widely facilitated and proliferated by digital techniques, digital multimedia can be easily manipulated without leaving any obvious clue. Steganalysis and forgery detection are two interesting areas with broad impact to each other. While multiple promising and well-designed steganalysis methods have been proposed and several steganographic systems have been successfully steganalyzed, the advance in forgery detection may trail behind.
As a standardized lossy compression, JPEG is the most popular digital image format and standard in our daily life. JPEG image-based forensics has become one of hot spots in multimedia forensics. In terms of the manipulation of JPEG image forgery, generally, the tampering involves several basic operations, such as image resize, rotation, splicing, double compression. The detection of these fundamental manipulations and relevant forgery has been well studied. For example, double JPEG compression is one of most adopted manipulations.
In some cases, the bit stream of a JPEG image is decoded and the manipulation is implemented in spatial domain. The modified image is then compressed back to JPEG format. If the newly adopted quantization table is different from the one used by original JPEG image, the modified JPEG image may be said to have undergone a double JPEG compression. Although JPEG based double compression does not by itself prove malicious or unlawful tampering, it is an evidence of image manipulation.
Some detection methods have been proposed for JPEG double compression, one of common operations that may occur in the tampering manipulation. When the quality of the second compression is higher than the quality of the first compression, some existing methods have obtained good detection results. Existing methods may, however, fall short of accurately detecting the down-recompression when the second compression quality is lower than the first compression quality. A crafty forgery maker may take account of the weakness of the current detection arts, doctor images and produced them in a lower image quality, to escape from being detected.
Inpainting, also known as image completion, is the process to reconstruct lost or corrupted parts of images and videos. Though inpainting, originally designed to reconstruct lost or deteriorated parts of images and videos, inpainting has been used for image tampering, including region filling and object removal to disguise the meaning of objects or conceal the truth. While several types of tampering have been successfully exposed, few studies address the challenge of inpainting forgery in JPEG images.
There are many applications of the inpainting technique, ranging from film restoration, deterioration reverse, to image and video editing and restoration, including but not limited to removal of occlusions, such as texts, subtitles, stamps, logos, watermarks, wrinkles, and unwanted objects from digital images and/or videos. Most inpainting methods in the literature can be mainly classified into geometry- and texture-oriented methods. Geometry-oriented methods are performed by using a partial differential equation (POE), derived from variation principles, showing good performance in propagating smooth level lines or gradients, but undesirable in the presence of texture. Geometry-oriented methods are local in the sense since the PDEs only involve the interactions among neighboring pixels on the image grid. Texture-oriented methods model texture as a probabilistic graphical model. These methods may be referred to as exemplar-based approaches. Bugeau et al. has combined copy-paste texture synthesis, geometric PDEs and coherence among neighboring pixels and proposed a comprehensive framework for image inpainting, being able to approximately minimize proposed energy function.
Several inpainting tools are currently available on the Internet. Cyber criminals may easily obtain these inpainting tools to disguise objects and conceal the truth of digital photos, which might be presented as important evidences for legitimate purposes. As such, there is a heightened need to detect such tampering in digital JPEG images. Several methods have been proposed for JPEG-based forensics, such as the detection of image resize, splicing, double compression and duplication detection. However, regarding the detection of inpainting-based forgery in digital images, such detection is believed to be still underexplored.
Generally, after inpainting manipulation, post-combination attacks can be employed to cover or compromise original inpainting traces. It is very hard to model the processing by inpainting followed by these attacks. Existing methods and system may not be effective in exposing the inpainting forgery from these subsequent combination attacks.
Seam carving, also known as image retargeting, content-aware scaling, liquid resizing or liquid rescaling, is a method developed by Shai Avidan and Ariel Shamir for image resizing. The idea behind the image resizing is to establish a number of paths of least importance, called seams in an image or video file for both reduction and expansion. A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right. Seam carving allows manually defining areas in which pixels may not be changed and features the ability to erase entire objects from an image/photo. Seam carving has been implemented in Adobe Photoshop and other popular computer graphic applications including GIMP, digiKam, ImageMagic, and iResizer. The proliferation of seam carving raises a serious challenge in image forensics.
Although several detectors have been used to detect seam carving-based image forgery, the effort to expose the tampering of low quality images is still missing. A crafty forgery maker may save doctored images/photos into a low quality since it is very difficult to expose the forgery in low quality images.
The methods presented herein address the challenges inherent in detecting forgery in images, particularly low quality JPEG images.